Abstract
Users from all over the world increasingly adopt social media for newsgathering, especially during breaking news. Breaking news is an unexpected event that is currently developing. Early stages of breaking news are usually associated with lots of unverified information, i.e., rumors. Efficiently detecting and acting upon rumors in a timely fashion is of high importance to minimize their harmful effects. Yet, not all rumors have the potential to spread in social media. High-engaging rumors are those written in a manner that ensures achievement of the highest prevalence among the recipients. They are difficult to detect, spread very fast, and can cause serious damage to society. In this article, we propose a new multi-task Convolutional Neural Network (CNN) attention-based neural network architecture to jointly learn the two tasks of breaking news rumors detection and breaking news rumors popularity prediction in social media. The proposed model learns the salient semantic similarities among important features for detecting high-engaging breaking news rumors and separates them from the rest of the input text. Extensive experiments on five real-life datasets of breaking news suggest that our proposed model outperforms all baselines and is capable of detecting breaking news rumors and predicting their future popularity with high accuracy.